In recent years, the issues related to public safety and security have increased significantly, which resulted in a surge of demand for automated surveillance systems. However, traditional monitoring systems based on CCTV require constant human surveillance, which is not only wasteful but also error- prone. This paper proposes a deep learning-based surveillance system that can automatically detect suspicious activities in videos. The proposed model utilizes CNNs to classify video frames into normal and suspicious categories. Upon detection of suspicious activity, the system captures the frame and sends an automated email notification to the registered system administrator using the SMTP protocol. The proposed system utilizes OpenCV for video processing, TensorFlow/Keras for training and predicting the models, and SQLite to securely store administrator information within a database.